Television receivers typically demodulate and video decode (NTSC, PAL, etc.) radio frequency (RF) broadcast video signals, transmitted over the air or distributed over a cable television (CATV) system. The resulting video signals are then used to drive a display device on which the resulting video images are shown. In such receivers, the picture quality may be impaired by channel noise, which typically consists of Gaussian noise whose spectrum is shaped by the transmission channel and by the demodulation and decoding of the video signal. Video processing systems are generally used to improve the quality of the displayed picture when the decoded video signals have been impaired, as they are when channel noise is present.
A number of well-known video processing algorithms are used for image noise reduction. Some of these algorithms perform noise reduction in the spatial domain using so-called “sigma filters.” These algorithms require an estimate of the image noise amplitude in order to adaptively perform a level of filtering that reduces the effect of the noise on the image without degrading any more detail than is necessary. The image noise estimations, however, are very computationally intensive, and therefore, difficult to perform at the high speeds required for real-time video processing.
An example of such an image noise estimation algorithm is described in U.S. Pat. No. 5,657,401 issued to De Haan et al. The method and apparatus of U.S. Pat. No. 5,657,401 essentially obtains simple sums of the absolute values of the differences (SADs) between adajcent pixels, and determines whether each sum falls within a specified range, whose lower and upper bounds are both determined by the current value of the noise estimate. The number of these SADs that fall within the range is then counted over the current video image. If the count exceeds a specified threshold number, the current noise estimate is considered valid. If not, a different estimate must be tried. However, because the noise estimate is precisely what this algorithm must determine, successive “trial” values of the noise estimate must be used until the correct value is found. Each estimate typically requires one video image, which in practice usually consists of an interlaced field, so that at most two such estimates can be performed for each video frame. Thus, this method undesirably requires an indeterminate number of frames before it converges to the correct result, and the entire procedure may need to be repeated if the noise level changes (even by a small amount), once again requiring an indeterminate number of frames to converge to the new result.
Since present noise estimation methods are typically implemented in hardware, a noise estimation method that converges in one image and can be efficiently implemented in hardware is greatly desirable.